The effects of oral arginine on its metabolic pathways in Sprague–Dawley rats
Bibliographic record
Abstract
Oral arginine supplements are popular mainly for their presumed vasodilatory benefit. Arginine is a substrate for at least four enzymes including nitric oxide synthase (NOS) and arginase, but the impact of oral supplements on its different metabolic pathways is not clear. Deficiencies of arginine-metabolising enzymes are associated with conditions such as hyperammonaemia, endothelial dysfunction, central nervous system and muscle dysfunction, which complicate the use of oral arginine supplements. We examined the effect of l-arginine (l-Arg) and d-arginine (d-Arg), each at 500 mg/kg per d in drinking water administered for 4 weeks to separate groups of 9-week-old male Sprague-Dawley rats. We quantified the expression of enzymes and plasma, urine and organ levels of various metabolites of arginine. l-Arg significantly decreased cationic transporter-1 expression in the liver and the ileum and increased endothelial NOS expression in the aorta and the kidney and plasma nitrite levels, but did not affect the mean arterial pressure. l-Arg also decreased the expression of arginase II in the ileum, arginine:glycine amidinotransferase in the liver and the kidney and glyoxalase I in the liver, ileum and brain, but increased the expression of arginine decarboxylase and polyamines levels in the liver. d-Arg, the supposedly inert isomer, also unexpectedly affected the expression of some enzymes and metabolites. In conclusion, both l- and d-Arg significantly affected enzymes and metabolites in several pathways that use arginine as a substrate and further studies with different doses and treatment durations are planned to establish their safety or adverse effects to guide their use as oral supplements.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".